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Luxembourg

  • Monarch:Henri
  • Prime Minister:Xavier Bettel
  • Capital city:Luxembourg
  • Languages:Luxembourgish (official administrative and judicial language and national language (spoken vernacular)) 88.8%, French (official administrative, judicial, and legislative language) 4.2%, Portuguese 2.3%, German (official administrative and judicial language) 1.1%, other 3.5% (2011 est.)
  • Government
  • National statistics office
  • Population, persons:6,07,728 (2018)
  • Area, sq km:2,430
  • GDP per capita, US$:1,14,340 (2018)
  • GDP, billion current US$:69.5 (2018)
  • GINI index:No data
  • Ease of Doing Business rank:66

Loans

All datasets:  E F G I N P S
  • E
    • अक्तूबर 2019
      Source: European Central Bank
      Uploaded by: Knoema
      Accessed On: 30 अक्तूबर, 2019
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      ECB: Risk Assessment Indicators (RAI), Monthly Update
    • मार्च 2017
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 03 अप्रैल, 2017
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      This is a one-off survey, not intended to be repeated.  Access to finance is crucial to business success and an important factor for economic growth in Europe following the economic crisis in 2007. The purpose of the survey is to:Examine where there may be constraints on the availability of finance, and how those may be changing.Provide evidence on the need for finance (loans, equity and other) in the future, for example to promote growth.Identify the sources from which businesses would wish to obtain this finance.  It is important to be able to compare businesses that have shown sharp growth in recent years with those that have not; and to separate more recently formed businesses from others. As a consequence, the survey will collect information separately for "gazelles" (defined at page 3 below); other high-growth businesses that have been established longer than gazelles; and other businesses. Only businesses with an employment of 10-249 in 2005 and at least 10 employees in 2010 are being covered.
  • F
    • नवम्बर 2019
      Source: International Monetary Fund
      Uploaded by: Knoema
      Accessed On: 18 नवम्बर, 2019
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      The Financial Soundness Indicators (FSIs) were developed by the IMF, together with the international community, with aim of supporting analysis and assessing strengths and vulnerabilities of financial systems. The Statistics Department of the IMF, disseminates data and metadata on selected FSIs provided by participating countries. For a description of the various FSIs, as well as the consolidation basis, consolidation adjustments, and accounting rules followed, please refer to the concepts and definitions document in the document tab. Reporting countries compile FSI data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata to conduct more meaning cross-country comparisons or to assess the evolution of a given FSI for any of the countries.
    • जून 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Knoema
      Accessed On: 04 जून, 2019
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      National Accounts - Volume IIIa - Financial Accounts - Flows, which record, by type of financial instruments, the financial transactions between institutional sectors, and are presented in two tables: Financial accounts, consolidated and Financial accounts, non-consolidated.
  • G
    • जुलाई 2019
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 05 जुलाई, 2019
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      Consolidated banking data (CBD) contain information on the aggregate consolidated profitability, balance sheets, asset quality, liquidity and solvency of EU banks, and refer to all EU Member States. CBD are a key component of the ECB/ESCB (European System of central Banks) statistical toolbox for financial stability analysis and are also one of the main inputs to the statistical support provided by the ECB to the European Systemic Risk Board (ESRB). Two indicators retrieved from CBD have been added to the set of MIP auxiliary indicators starting from the 2019 exercise. The MIP auxiliary indicators retrieved from CBD are:   Gross non-performing loans of domestic and foreign entities as percentage of gross loans, annual data (tipsbd10) A loan, other than held for trading, is considered as non-performing if satisfies either or both of the following criteria: (a) It is a material loan which is more than 90 days past-due; (b) The debtor is assessed as unlikely to pay its credit obligations in full without realisation of collateral, regardless of the existence of any past-due amount or of the number of days past-due. Non-performing loans include defaulted and impaired loans and since end-2014 have followed the harmonised definition of the European Banking Authority (EBA) used for supervisory reporting. The MIP indicator is defined as total gross non-performing loans and advances as % of total gross loans and advances (gross carrying amount), for the reporting sector "domestic banking groups and stand-alone banks, foreign controlled subsidiaries and foreign controlled branches, all institutions". The indicator is consistent with the macro-financial focus of MIP surveillance and provides complementary information to assess private debt, which features among the variables in the headline scoreboard.   Consolidated banking leverage, annual data (tipsbp20). The indicator, covering the banking sector only, is defined as total assets divided by total equity, for the reporting sector "domestic banking groups and stand-alone banks, foreign controlled subsidiaries and foreign controlled branches, all institutions, full sample (all banking groups / stand-alone banks irrespective of their accounting / supervisory framework)". Data on domestically controlled banks are consolidated across borders and sectors at the prudential perimeter of consolidation. The leverage indicator based on the CBD data, is used to complement the reading of the scoreboard: it has clear economic interpretation, is comparable across countries, and is consistently based on book values.   For both indicators, data on domestically controlled banks are consolidated across borders and sectors at the prudential perimeter of consolidation.
  • I
  • N
    • जुलाई 2019
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 13 जुलाई, 2019
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      The data on contingent liabilities and potential obligations of government are collected in the context of the Enhanced Economic Governance package (the "six-pack") adopted in 2011. In particular, Council Directive 2011/85 on requirements for budgetary frameworks of the Member States requires the Member States to publish relevant information on contingent liabilities with potentially large impacts on public budgets, including government guarantees, non-performing loans, and liabilities stemming from the operation of public corporations, including the extent thereof. The liabilities are called “contingent” in the sense that they are by nature only potential and not actual liabilities. Non-performing loans could imply a potential loss for government if these loans were not repaid. This new data collection represents a step towards further transparency of public finances in the EU by giving a more comprehensive picture of EU Member States’ financial positions3 It is to be underlined that contingent liabilities are not part of the general government (Maastricht) debt as defined in the Council Regulation (EC) No 479/2009 of 25 May 2009 on the application of the Protocol on the excessive deficit procedure annexed to the Treaty establishing the European Community. Eurostat collects and publishes the following indicators: government guarantees, liabilities related to public-private partnerships recorded off-balance sheet of government, liabilities of government controlled entities classified outside general government (public corporations) and non-performing loans. Regarding government controlled entities, it should be mentioned that this refers to  government controlled units, not classified in general government, and which are controlled, directly or indirectly (through other public units), by government. In cases when the government share in a corporation is lower than 50% and government does not have control over an entity, the corporation is not considered as controlled by government. Regarding the control criteria, according to ESA 2010 paragraph 20.18: “Control over an entity is the ability to determine the general policy or programme of that entity (…)”. The criteria to be used for corporations are indicated in ESA 2010 paragraphs 2.38 and further detailed in paragraph 20.309. ESA 2010 paragraph 2.38 specifies that: “General government secures control over a corpo­ration as a result of special legislation, decree or reg­ulation which empowers the government to deter­mine corporate policy. The following indicators are the main factors to consider in deciding whether a corporation is controlled by government:(a) government ownership of the majority of the voting interest; (b) government control of the board or governing body; (c) government control of the appointment and removal of key personnel;(d) government control of key committees in the entity; (e) government possession of a golden share; (f) special regulations; (g) government as a dominant customer; (h) borrowing from government. A single indicator may be sufficient to establish control, but, in other cases, a number of separate indicators may collectively indicate control.”
  • P
    • नवम्बर 2019
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 14 नवम्बर, 2019
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      The Private sector credit flow represents the net amount of liabilities (debt securities (F.3) and loans (F4)) in which the sectors Non-Financial corporations (S.11) and Households and Non-Profit institutions serving households (S.14_S.15) have incurred along the year. Financial flows and stocks data are often referred to collectively in the national accounts framework as 'financial accounts'. Financial flows consist of transactions and other flows, and represent the difference between the opening financial balance sheet at the start of the year and the closing balance sheet at the end of the year. The data are compiled in accordance with the European System of Accounts (ESA 2010), which came into force in September 2014. The MIP scoreboard indicator is the consolidated Private sector credit flow, as percentage of GDP. For the MIP purposes are published annual consolidated and non-consolidated data by institutional sectors and financial instruments.
    • नवम्बर 2019
      Source: Eurostat
      Uploaded by: Knoema
      Accessed On: 14 नवम्बर, 2019
      Select Dataset
      The Private sector credit flow represents the net amount of liabilities (debt securities (F.3) and loans (F4)) in which the sectors Non-Financial corporations (S.11) and Households and Non-Profit institutions serving households (S.14_S.15) have incurred along the year. Financial flows and stocks data are often referred to collectively in the national accounts framework as 'financial accounts'. Financial flows consist of transactions and other flows, and represent the difference between the opening financial balance sheet at the start of the year and the closing balance sheet at the end of the year. The data are compiled in accordance with the European System of Accounts (ESA 2010), which came into force in September 2014. The MIP scoreboard indicator is the consolidated Private sector credit flow, as percentage of GDP. For the MIP purposes are published annual consolidated and non-consolidated data by institutional sectors and financial instruments.
  • S
    • जून 2019
      Source: Organisation for Economic Co-operation and Development
      Uploaded by: Sandeep Reddy
      Accessed On: 18 जून, 2019
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      The dataset on Statistical discrepancy (Institutional Investors – Financial Balance Sheets) represents the time series of the dataset on Institutional investors' assets and liabilities (7II) along with those of the dataset on Financial Balance Sheets (720), for the financial instruments and institutional sectors which are in common to these two datasets.  Additionally, for each of the above-mentioned time series, a statistical discrepancy is reported in order to show any possible differences which may exist between the two datasets (7II and 720).  In fact, the dataset on Institutional investors' assets and liabilities (7II) constitutes an attempt to better integrate these data in the framework of the System of National Accounts 2008 (SNA 2008).  However, discrepancies may exist and may, for example, be caused by balancing practices (e.g. when sector and counterpart sector data are reconciled) in the compilation of Financial Balance Sheets at a higher level of aggregation, which may not have been carried through at a lower level of aggregation. Moreover, differences may also be caused by the use of different source data.

हमारी गोपनीयता कथन और कुकी नीति

"हमारी वेबसाइट आपके ऑनलाइन अनुभव को बेहतर बनाने के लिए कुकीज़ का उपयोग करती है। जब आपने यह वेबसाइट लॉन्च की, तो उन्हें आपके कंप्यूटर पर रखा गया था। आप अपने इंटरनेट ब्राउज़र सेटिंग्स के माध्यम से अपनी व्यक्तिगत कुकी सेटिंग्स बदल सकते हैं।"

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